A Novel Approach to Handle Inference in Discrete Markov Networks with Large Label Sets

Alexander Oliver Mader, Jens Berg, Cristian Lorenz, Carsten Meyer
Proceedings of the Ninth International Conference on Probabilistic Graphical Models, PMLR 72:249-259, 2018.

Abstract

MAP inference over discrete Markov networks with large label sets is often applied, e.g., in localizing multiple key points in the image domain. Often, approximate or domain specific methods are used to make the problem feasible. An alternative method is to preselect a limited (much smaller) set of suitable labels, which bears the risk to exclude the correct solution. To solve the latter problem, we propose a two-step approach: First, the reduced label sets are extended by a novel “refine” label, which — when chosen during inference — marks nodes where the label set is insufficient. The energies for this additional label are learned in conjunction with the network’s potential weights. Second, for all nodes marked with the “refine” label, additional local inference steps over the full label set are performed. This greedy refinement becomes feasible by extracting small subgraphs around the marked nodes and fixing all other nodes. We thoroughly evaluate and analyze our approach by solving the problem of localizing and identifying 16 posterior ribs in 2D chest radiographs.

Cite this Paper

BibTeX
@InProceedings{pmlr-v72-mader18a, title = {A Novel Approach to Handle Inference in Discrete Markov Networks with Large Label Sets}, author = {Mader, Alexander Oliver and von Berg, Jens and Lorenz, Cristian and Meyer, Carsten}, booktitle = {Proceedings of the Ninth International Conference on Probabilistic Graphical Models}, pages = {249--259}, year = {2018}, editor = {Kratochvíl, Václav and Studený, Milan}, volume = {72}, series = {Proceedings of Machine Learning Research}, month = {11--14 Sep}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v72/mader18a/mader18a.pdf}, url = {https://proceedings.mlr.press/v72/mader18a.html}, abstract = {MAP inference over discrete Markov networks with large label sets is often applied, e.g., in localizing multiple key points in the image domain. Often, approximate or domain specific methods are used to make the problem feasible. An alternative method is to preselect a limited (much smaller) set of suitable labels, which bears the risk to exclude the correct solution. To solve the latter problem, we propose a two-step approach: First, the reduced label sets are extended by a novel “refine” label, which — when chosen during inference — marks nodes where the label set is insufficient. The energies for this additional label are learned in conjunction with the network’s potential weights. Second, for all nodes marked with the “refine” label, additional local inference steps over the full label set are performed. This greedy refinement becomes feasible by extracting small subgraphs around the marked nodes and fixing all other nodes. We thoroughly evaluate and analyze our approach by solving the problem of localizing and identifying 16 posterior ribs in 2D chest radiographs.} }
Endnote
%0 Conference Paper %T A Novel Approach to Handle Inference in Discrete Markov Networks with Large Label Sets %A Alexander Oliver Mader %A Jens Berg %A Cristian Lorenz %A Carsten Meyer %B Proceedings of the Ninth International Conference on Probabilistic Graphical Models %C Proceedings of Machine Learning Research %D 2018 %E Václav Kratochvíl %E Milan Studený %F pmlr-v72-mader18a %I PMLR %P 249--259 %U https://proceedings.mlr.press/v72/mader18a.html %V 72 %X MAP inference over discrete Markov networks with large label sets is often applied, e.g., in localizing multiple key points in the image domain. Often, approximate or domain specific methods are used to make the problem feasible. An alternative method is to preselect a limited (much smaller) set of suitable labels, which bears the risk to exclude the correct solution. To solve the latter problem, we propose a two-step approach: First, the reduced label sets are extended by a novel “refine” label, which — when chosen during inference — marks nodes where the label set is insufficient. The energies for this additional label are learned in conjunction with the network’s potential weights. Second, for all nodes marked with the “refine” label, additional local inference steps over the full label set are performed. This greedy refinement becomes feasible by extracting small subgraphs around the marked nodes and fixing all other nodes. We thoroughly evaluate and analyze our approach by solving the problem of localizing and identifying 16 posterior ribs in 2D chest radiographs.
APA
Mader, A.O., Berg, J., Lorenz, C. & Meyer, C.. (2018). A Novel Approach to Handle Inference in Discrete Markov Networks with Large Label Sets. Proceedings of the Ninth International Conference on Probabilistic Graphical Models, in Proceedings of Machine Learning Research 72:249-259 Available from https://proceedings.mlr.press/v72/mader18a.html.

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